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Upscaling UAV-borne high resolution vegetation index to
satellite resolutions over a vineyard Y. Wang
1, D. Ryu
1, S. Park
1, S. Fuentes
2 and M. O’Connell
3
1 Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, Australia 2 School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of
Melbourne, Parkville, Victoria, Australia 3Department of Economic Development, Jobs, Transport and Resources, Tatura, Victoria, Australia
Email: [email protected]
Abstract: Vegetation indices retrieved by optical satellite sensors provide important information related
to the vigour, biomass and growth of plants. Homogeneous biophysical state within a satellite footprint is
often assumed to make the spectral vegetation indices a reliable predictor of the vegetation condition.
However, the image pixel resolution for the most widely used optical satellites, such as Landsat (30 m
by 30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m by 250 m ~ 500 m by
500 m), includes vegetation canopies with varying degree of heterogeneity within single pixels. This pixel-
level heterogeneity makes it difficult to predict individual plant-level vegetation biomass and vigour from
satellite imagery. In particular, the structured nature of vineyard fields comprising predominantly of bare soil
and grapevines pose challenges when estimating the vine-level biomass and vigour from the coarse-
resolution satellite images, that present signals averaged over vine canopies and non-canopies (e.g., soil). In
this study, we examine how the normalized difference vegetation index (NDVI) varies with aggregating pixel
scales from sub-5-cm to 30-m and 250-m resolutions using very-high-resolution images collected by a
multispectral imaging sensor on-board an unmanned aerial vehicle (UAV). The aggregated 30-m and
250-m scale NDVI values were compared with Landsat 8 (30 m) and MODIS (250 m) products collected from a commercial vineyard in Victoria, Australia within a narrow time window (25 hours). Results showed that when upscaling the high-resolution NDVI values (by aggregating input multispectral bands for NDVI) to coarser resolutions in highly structured vineyard fields, NDVI decreased initially up to the field heterogeneity scale (spacing between vine rows in this study site) and then levelled off beyond the scale. Furthermore, the difference between NDVIs at Landsat 8 pixels was mainly caused by the areal fractions of canopy and soil pixels and the NDVI values of the soil rows.
Keywords: Normalized Difference Vegetation Index (NDVI), Unmanned Aerial Vehicle (UAV),
pixel aggregation, upscaling imagery
22nd International Congress on Modelling and Simulation, Hobart, Tasmania, Australia, 3 to 8 December 2017 mssanz.org.au/modsim2017
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Figure 1. Study area of Curly Flat
Vineyard on March 30, 2015 (mosaic
image from eBee)
Figure 1. Study area of Curly Flat
Vineyard
Table 1. Attributes of different sensors used to derive NDVI
Sensor UAV (eBee) LANDSAT 8 Level 2 AQUA MODIS Vegetation
Indices
Date 30 March 2015 29 March 2015 30 March 2015
Data Source Field Survey USGS USGS
Captured Time (Local time) 11:30 am 12:30 pm 1:30 pm
Red/NIR Band Wavelength (μm) 0.62 ± 0.05 / 0.83 ± 0.04 0.65 ± 0.02 / 0.87 ± 0.02 0.64 ± 0.02 / 0.86 ± 0.02
Ground Spatial Resolution 4 cm 30 m 250 m
Wang et al., Upscaling UAV-borne high resolution vegetation index to satellite resolutions over a vineyard
1. INTRODUCTION
Accurate monitoring of vegetation vigour and biomass over large fields is an important component of
precision agriculture. Over large agricultural fields, satellite remote sensing can provide a suitable coverage
with a moderate revisit time. The spatial and temporal coverage of satellite observations can be enhanced by
combining multiple products that have different spatial resolutions and instrument features. However, it is
important to understand inherent discrepancy between different products originating from the spatial, spectral
and other instrument features since they can influence the scientific outputs, from different satellites or other
remote sensing platforms. For example, Abuzar et al (2014) demonstrated difference in NDVI values from
different platforms over horticulture crops in south-eastern Australia.
Multispectral sensors on-board the Unmanned Aerial Vehicles (UAVs) have been attracting significant
attention as a versatile platform to capture high-resolution imagery in a fast and economic way to support
agricultural management decisions. Utility of UAV-borne sensing is remarkable particularly for small scale
applications (Grenzdorffer et al., 2008). Also, the high-resolution imagery obtained by UAVs provides a
unique opportunity to examine the effect of aggregating pixel scales, from centimetres to the scales of
popular optical satellites such as Landsat (30 m by 30 m) and MODIS (250 m by 250 m ~ 500 m by 500 m).
The main aim of this study is to address how vegetation indices vary across different pixel scales among
canopy and non-canopy, respectively. For this purpose, NDVI values retrieved from multiple remote sensing
platforms at different scales, from sub-5-cm (UAV) to 250-m (MODIS) are compared. In the course of the
multi-scale comparisons, following questions are aimed to be addressed: what is the influence of changing
pixel scales on NDVI?; what are the main factors to be considered when estimating sub-grid-scale
(individual plant level) NDVI from coarser scales at which heterogeneous land cover types of structured
horticultural landscapes are lumped? To achieve the study objectives, this paper employs three
representative scales imagery applying NDVI in a vineyard, including the imagery captured from UAV
(eBee, senseFly, Ltd.), Landsat 8 and MODIS on-board the Aqua satellite for comparison.
2. MATERIALS AND METHODOLOGY2.1. Study area
The study area is located at the Curly Flat Vineyard
(37017′40″S, 144
042′24″E) with average elevation of 560
metres in the Macedon Ranges of Victoria, Australia. It covers
approximately 14 hectares with progressively high vegetation
coverage since first plantings in 1992, including 70% Pinot
Noir, 25% Chardonnay and 5% Pinot Gris. All vines are
grafted with two main scaffold branches known as Lyre Trellis
with an inter-plant space of 0.9 m. Seven highly structured
vineyard fields numbered from 1 to 7 in Figure 1 with a row
and plant space as 3 m and 1.8 m, respectively, provided an
ideal study area to conduct upscaling analysis over fields with a
large contrast among different vegetation fractions.
2.2. Data Acquisition
Three different scales of cloud-free imagery were obtained
from UAV, Landsat and MODIS to map NDVI of the study area between the 29th
and 30th
of March 2015, as
described in Table 1.
N
1
2
3
4 5
6 7
100 m
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Wang et al., Upscaling UAV-borne high resolution vegetation index to satellite resolutions over a vineyard
Figure 2. False color UAV imagery
captured GCPs and calibration
targets placed between Field 3 and 4
(1)
2.3. Methods
Calculation of Vegetation Indices (VI) is a direct way to
quantify the density of plant growth. In general, multiple
spectral bands are combined in producing VIs to represent
vegetation vigour and biomass. In fact, the reflectance of plants
in the near-infrared (NIR) range is much higher than that in the
visible band, especially the red band. Therefore, the contrast
between responses of NIR and red band can be considered as a
sensitive measurement for green leaves (Solano et al., 2010). In
this study, the most commonly used NDVI to produce spectral
VI to separate green leaves from the bare soil and water bodies
to minimize topographic effects was implemented (Silleos et al.,
2006).
In order to reduce the impact from difference between sensors
and to improve the accuracy, it is necessary to conduct
radiometric calibration and geometric rectification before
calculating the NDVI for each image to correct radiometric
errors and geometric distortion. A total of 280 high-resolution UAV images over the Curly Flat Vineyard
were georeferenced with 16 ground control points (GCPs). An empirical linear radiometric calibration was
performed using known reflectance values over three artificial calibration targets, placed near the centre of
the Vineyard as shown in Figure 2. The polyester target fabric represented three different constant reflectance
values of 33%, 12% and 6%, respectively. The digital number (DN) was converted to calibrate reflectance by
the grey scale calibration using the linearity of reflectance for NIR and red bands. The NDVI is a normalized
ratio of the surface reflectance of NIR and red bands, which can separate green vegetation from other
surfaces, shown in Equation (1).
𝑁𝐷𝑉𝐼 =𝜌𝑁𝐼𝑅 − 𝜌𝑅
𝜌𝑁𝐼𝑅 + 𝜌𝑅
The calculated NDVI value ranges from -1 to +1. The value close to 1 is related to the highest possible
density of green vegetation, while the value for bare soil, water bodies and clouds are near 0 (Weier and
Herring, 2009). This study distinguished the canopy and non-canopy (e.g., soil background) pixels based on
the NDVI distribution with a threshold value of 0.52. Additionally, the threshold NDVI value of Landsat
pixel was 0.7 for high vegetation pixels, 0.6 for intermediate vegetation pixels and less than 0.5 for low
vegetation pixels. After calculating individual NDVI values, it was assessed how the NDVI varied among
different-scale platforms by conducting resampling from small scales to large scales in gradual levels. When
upscaling UAV imagery to the Landsat scale, we resampled the reflectance of each input band (red and NIR
bands) in 6 increments of 10 cm, 50 cm, 1 m, 3 m, 10 m and 30 m, then the resampled reflectance for red and
NIR bands was used to calculate NDVI values in each corresponding scale. The same method was applied to
aggregate pixels from the Landsat scale to the MODIS scale in three increments 100 m, 200 m and 250 m.
The upscaling of spatial resolution involved the linear aggregation of high-resolution imagery into coarser
resolution with fewer pixels, which naturally smears out details of the original data (Jones and Vaughan,
2010). In this work, ENVI 5.3 (Harris, Inc.), ArcGIS 10.3 (Esri, Inc.) and PhotoScan (Agisoft, LLC.) were
used to process, analyse and integrate the images.
3. RESULTS AND DISCUSSION
3.1. NDVI maps from different platforms
The NDVI maps derived from UAV, Landsat 8 and MODIS of the entire study area are shown in Figure 3 in
an NDVI range of 0 to 1. As presented in the NDVI maps of three different scales, UAV and Landsat
preserved a better contrast between water (close to 0), soil (approximately 0.4) and canopy (close to 1). On
the other hand, the coarser-scale MODIS NDVI provides only general information of vegetation existence
with NDVI values varying between 0.3 and 0.6. In general, the NDVI values are intermediate between the
values of soil and vegetation canopy, and exhibits lower values over the northwest and southern parts of the
study area where the bare soil fraction is dominant. However, the NDVI map of the Landsat scale shows the
boundary of each subfield more clearly than MODIS scale, making it possible to distinguish between
vineyard fields (green), bare soil (yellow) and the open water body (brown). The Landsat data shows the
highest NDVI over Field 1 (field number as represented in Figure 1) among all the seven fields. Additionally,
compared with the coarser NDVI maps, the finest NDVI from UAV provides a good representation of the
GCP
6%
12%
33%
N
5 m
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Wang et al., Upscaling UAV-borne high resolution vegetation index to satellite resolutions over a vineyard
Figure 3. NDVI maps from different platforms in the study area
(b)
(a)
Figure 4. (a) Comparison NDVI map in Field 3 and (b) NDVI values in a specific area from
Landsat platform
details in the highly structured grapevine fields. For example, in Field 3 as shown in Figure 4, the individual
highly structured vine rows and even individual vines can be recognised from the UAV scale. From the
Landsat scale, some intra field spatial variation in NDVI can be visualized, where it was higher in the west
compared to the east in Figure 4 (a). Greater detail in spatial variability of NDVI was recognized from the
UAV scale, where the high NDVI values were clustered in the west than the east. More specifically, the
central part of the Field 3 in the white dash-line boundary, the NDVI showed slightly higher values than its
adjacent pixels in the same column in Figure 4 (b). From the UAV scales, higher fraction of canopy cluster
was found to the left in the white dotted box, which matched the changes in the Landsat pixel. However, it is
only possible to get a very rough idea of the mean NDVI in a large area for MODIS compared to the smaller
scales.
3.2. Upscaling from UAV to Landsat 8 resolution
After comparing the NDVI values from three different platforms at the pixel scales, 4 cm, 30 m and 250 m,
we conducted a systematic aggregation of the UAV imagery to examine how the NDVI values vary across
the scales. Analysis of NDVI changes with reducing resolution (pixel aggregation) was applied to six Landsat
8 pixels randomly chosen in three intervals of NDVI values (2 pixels for each interval as shown in Figure 5):
H represents high vegetation pixels (NDVI > 0.7), M denotes intermediate vegetation pixels (NDVI ≈ 0.6)
and L stands for low vegetation pixels (NDVI < 0.5) of Landsat 8.
0.6972 0.6591 0.6330 0.6223 0.5031
0.7253 0.6730 0.6597 0.6376 0.5597
0.7109 0.6681 0.6421 0.6499 0.5798
0.7021 0.6769 0.6096 0.6317 0.5538
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Wang et al., Upscaling UAV-borne high resolution vegetation index to satellite resolutions over a vineyard
(a) High Vegetation Pixels (b) Intermediate Vegetation Pixels (c) Low Vegetation Pixels
Figure 6. Upscaling UAV NDVI in different mean NDVI level sampling grids
0.4
0.6
0.8
0 10 20 30
ND
VI
Pixel Size (m)
0.4
0.6
0.8
0 10 20 30
ND
VI
Pixel Size (m) UAV Landsat 8
0.4
0.6
0.8
0 10 20 30
ND
VI
Pixel Size (m)
Figure 5. (a) Distribution of random selected pixels in three different vegetation levels in the
Landsat 8 NDVI (high, intermediate and low); (b) an example of high NDVI grid (30 m by 30 m)
(H1); (c) intermediate NDVI grid (M1); (d) low NDVI grid (L1)
High NDVI Grid
The high-NDVI pixels of Landsat 8, two adjacent pixels H1 and H2, both had NDVI of 0.72 were located in
Field 1. As shown in the UAV-borne NDVI maps of the sample pixels in Figures 5 (b) – (d), the high-NDVI
is mainly attributed to the denser canopies and soil rows of the block. The fractions of soil and canopy pixels
were 0.60 and 0.40, and the corresponding averaged NDVI values of them were 0.47 and 0.94, respectively.
The mean NDVI for the highest resolution of the UAV-borne imagery over H pixels was lower than the
Landsat 8 NDVI by a small margin of 0.06. However, the UAV-borne NDVI declined systematically with
the pixel aggregation (0.04 m, 0.1 m, 0.5 m, 1 m, 3 m, 10 m and 30 m as shown in Figure 6 (a)). The
difference between the UAV-borne and Landsat 8 NDVI values increased at the scales from 0.04 m to 1 m
then levelled off from 3 m, resulting in the final difference in NDVI of 0.14 at 30-m resolution.
Intermediate NDVI Grid
The intermediate NDVI pixels, M1 (0.58 for the Landsat 8 pixel) and M2 (0.60), were chosen from the Fields
2 and 6, respectively. The averaged NDVI values of the UAV data in the intermediate grid (M) as 0.94 for
canopy and 0.44 for soil were similar to those from high grid as shown in Table 2. However, the pixel-
average NDVI values were lower for M due to the smaller canopy fractions as 0.32 (smaller and sparser
canopy coverage). The mean UAV NDVI values are close to the Landsat 8 NDVI when the high-resolution
(0.4 m and 0.1 m) NDVI was used. But the mean NDVI again declines up to the 3-m resolution then levels
off to the minimum value (see Figure 6 (b)). The mean NDVI of UAV data, however, showed values slightly
higher than the Landsat 8 NDVI and the converged NDVI at over 3 m scales presented smaller difference
than the high-NDVI samples.
Low NDVI Grid
For the low NDVI pixels of L1 and L2, located on the edge of Fields 3 and 7, vegetation was represented less
strongly than higher vegetation pixels, since large bare soil surfaces (74% coverage) were included on the
edge of each L1 and L2 grid (Figure 5 (d)). The UAV NDVI showed two points of inflections when
upscaling NDVI at approximately 3 m and 10 m in pixel size (Figure 6 (c)), indicating two scales of
heterogeneity: (1) the size of inter-row distance (3 m), which is between a vine row and a soil row; (2) the
size of the bare soil area at the northern edge of the low vegetation grids. Since the first point of inflection (at
L1M1 H1 1
0
M1
L1
L2
H1
H2
M2
(b) H1 (High NDVI
grid)(c) M1 (Intermediate
NDVI grid)
(d) L1 (Low NDVI grid)(a) Landsat 8 NDVI
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Wang et al., Upscaling UAV-borne high resolution vegetation index to satellite resolutions over a vineyard
Figure 8. Location of the selected
MODIS pixel
Figure 9. Averaged NDVI comparison within a MODIS
pixel
0.4
0.425
0.45
0.475
0.5
0 50 100 150 200 250
ND
VI
Pixel Size (m) UAV Landsat 8 MODIS
Table 2. Statistics of individual NDVI within
selected Landsat pixels
…NDVI
Pixel
Averaged
Soil
Averaged
Canopy
Averaged
UAV
Landsat
8
H1 0.4706 0.9384 0.6526 0.7215
H2 0.4703 0.9413 0.6593 0.7268
M1 0.4456 0.9388 0.6019 0.5834
M2 0.4397 0.9402 0.6213 0.6030
L1 0.4078 0.9349 0.5812 0.4900
L2 0.4084 0.9190 0.5415 0.4591
3 m) is caused by the size of inter-row distance, high and intermediate UAV NDVIs also represented the
identical characteristics of inflection point at 3 m pixel size and turned to be stable (Figure 6 (a) and 6 (b)).
The mean UAV NDVI (L) was closer to the Landsat NDVI compared to higher vegetation pixels (H and M).
Overall of upscaled UAV NDVIs
For upscaling from UAV to Landsat 8, the averaged UAV NDVI in the Landsat pixel grid (30 m by 30 m)
decreased slightly until 3 m and then kept constant until 30 m scale. Furthermore, the difference of NDVI
values between UAV and Landsat was getting closer from higher to lower vegetation pixels. In other words,
the Landsat NDVI kept dropping until intersecting with UAV from higher to lower vegetation pixels when
increasing the pixel size from 0.04 m to 30 m. As shown in Table 2, the NDVIs of averaged canopy were
similar around 0.94 among high, intermediate and low vegetation pixels except for Landsat 8 pixel L2 (0.92),
but the NDVIs of averaged soil were more variable among Landsat 8 pixels. The latter shows that the
differences among the NDVIs were mainly affected by the relative fractions of soil and canopy areas in this
highly structured vine field. The pattern in Figure 7, which was located in the Fields 4 and 5, represents the
NDVI distribution from the original UAV scale, upscaled UAV to 3 m, upscaled UAV to 30 m and original
Landsat scale (30 m). The fractions of high NDVI and low NDVI were significant shown in this upscaling
UAV map. For example, the Fields 4 and 5 (Figure 7) includes an open area where bare soil surface and low
densities of vegetation exist in the boundary between two fields and the bottom of the fields. When
upscaling, NDVIs have merged and the details of NDVI have lost, however, the major pattern of the NDVIs
were still remained. In the result, the upscaled UAV NDVI showed similar values and spatial distributions to
the Landsat NDVI, showing the difference of 0.12.
3.3. Upscaling from UAV and Landsat 8 to MODIS scale
When upscaling from UAV and Landsat scales to MODIS scale, a centred pixel from MODIS was chosen as
the optimal one to cover the vines as much as possible. The pixel covered most of the Fields 4 and 5 and
some of the Fields 3 and 7, as highlighted in red parallelogram MODIS pixel with UAV NDVI map
underneath in Figure 8. In total, 23% of the pixels were covered with the highly structured vine canopy from
the UAV scale. The averaged NDVIs were 0.93 and 0.37 for canopy and soil in the selected MODIS pixel
size. And 0.49, 0.47 and 0.44 were the averaged NDVI for UAV, Landsat and MODIS, respectively.
Figure 7. NDVI scale comparisons from UAV to
Landsat 8 in Fields 4 and 5
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Wang et al., Upscaling UAV-borne high resolution vegetation index to satellite resolutions over a vineyard
For resampling pixel size from the UAV scale (0.04 m) to MODIS scale (250 m), it is possible to separate the
upscaling into two stages with the demarcation point at 100 m in Figure 9. The NDVI dropped to 0.44 in 10
m, from 10 m to 100 m it decreased 0.01 in a slight fluctuation, and then fell to 0.41 in 200 m with a steady
value to the end. Within 100 m, the fraction between the soil and canopy seemed to be stable in Field 4 and 5.
A significant change appeared when upscaling to 200 m, at which the soil fraction increased significantly.
Meanwhile, the Landsat NDVI declined gently and smoothly to meet MODIS when upscaling to MODIS
scale. From 30 m to 250 m, Landsat had the highest NDVI followed by MODIS and then the UAV.
4. CONCLUSIONS
This study used the UAV-borne high-resolution imagery to identify how NDVI varies when upscaling to
coarse-resolution satellite pixels over a high structured commercial vineyard. In terms of MODIS resolution
(250 m), it was not possible to distinguish the grapevine canopy, bare soil and other features within each sub-
field. For Landsat resolution (30 m), the details of the single canopy or vine-level structure were not
detectable. It was found that the averaged NDVI from UAV declined gradually to a steady value when
upscaling from the original resolution (0.04 m) to Landsat resolution (30 m). Meanwhile, the gap between the
averaged UAV NDVI and Landsat NDVI reduced from higher to lower vegetation pixels during upscaling
resample. For upscaling from Landsat to MODIS scale, the same tendency was showed with the NDVI that
dropped at the start and then kept invariant. While from UAV to MODIS scale, the NDVI started falling to a
constant value to reach the MODIS NDVI at 10 m, and from 200 m it decreased dramatically again to a
stable value. The possible reason for these changes might be associated to the resampled pixel location in an
area with different fraction of features, a large fraction of soil located beyond the field with high vine
structure.
Overall, the averaged canopy NDVIs extracted from UAV were similar across the H, M and L sampling grids
with different NDVI levels. However, NDVIs averaged over soil rows varied between the sampling grids,
and L pixels contained larger fraction of soil pixels with lower soil NDVIs. Therefore, it is concluded that the
difference between the upscaled NDVIs was mainly influenced by the soil NDVI and the areal fractions of
canopy to soil pixels. This study presented different spatial resolutions of NDVI upscaled from sub-
centimetre resolution and examined the comparisons between the upscaled UAV NDVI and the coarse scale
NDVIs from the Landsat and MODIS. As a future work, further research on various vegetation fields with
different types of spatial heterogeneity will be conducted to address how the upscaled NDVI varies according
to the aggregated pixel scales in general cases.
ACKNOWLEDGEMENTS
This research was supported by the Innovation Seed Fund for Horticulture Development grant from the
University of Melbourne and Department of Economic Development, Jobs, Transport and Resources,
Victoria. The author is gratefully thanks to Lisa Kimmorley for the access to Curly Flat Vineyard. And also
thanks to Andrew P. Nolan with the help of UAV dataset collection in the field.
REFERENCES
Abuzar, M., Sheffield, K., Whitfield, D., O’Connell, M., McAllister, A. (2014). Comparing inter-sensor
NDVI for the analysis of horticulture crops in south-eastern Australia. Int. Arch. American Journal of Remote Sensing, 2(1), 1-9.
Grenzdorffer, G. J., Engel, A., Teichert, B. (2008). The photogrammertric potential of low-cost UAVs in
forestry and agriculture. The International Archives of the Photogrammetry, Remote Sensing andSpatial
Information Sciences, 31(B3), 1207-1214.
Jones, H. G., Vaughan, R. A. (2010). Remote sensing of vegetation: Principles, techniques, and applications.
Oxford university press.
Silleos, N. G., Alexandridis, T. K., Gitas, I. Z., Perakis, K. (2006). Vegetation Indices: Advances Made in
Biomass Estimation and Vegetation Monitoring in the Last 30 Years, Geocarto International. 21(4), 21-
28.
Solano, R., Didan, K., Jacobson, A., Huete, A. (2010). MODIS Vegetation Indices (MOD13) C5
User’s Guide, Version, 2, 2010.
Weier, J., Herring, D. (2009). Measuring Vegetation (NDVI & EVI), Earth Observatory.
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